Prof.Hua Wang
Zhejiang University, China
Research Area: Finance, FinTech, BlockChain，Economics
Brief introduction: Professor, Ph.D. (University of Tokyo), CFA, Director of Financial Innovation and FinTech Research Center. Dr Wang has more than 10 years of professional and research experiences in international capital markets, financial products, FinTech, etc.,and 5 years as a senior Research Fellow at China Institute of Finance and Capital Markets. Dr. Wang has excellent proficiency in English and Japanese, with long-term international experience in Tokyo, London and Hong Kong, etc. He also participated in a number of research projects focusing on financial markets and government policies, and published tens of research articles and papers.
Title: TBC
Abstract:TBC
Prof. Yanhong Guo
Dalian University of Technology, China
Research Area: Management decision based on big data analysis; Swarm intelligence and decision making based on machine learning; Data driven besed marketing decions making in Internet
Brief introduction: Dr. Yanhong Guo is a professor at Dalian University of Technology, China, who is a committee member of Intelligent Service Committee of Chinese Artificial Intelligence Association and the committee member of the Big Data Committee of Chinese Computer Association. She obtained Ph.D. degree in Management Science and Engineering from Dalian University of Technology, China. In recent years, her research has focused on the application of machine learning and artificial intelligence methods in the field of smart finance. She has presided over 2 national projects including the National Natural Science Foundation of Chinese General Project and the National Natural Science Foundation of Chinese Youth Project. Her publishes are in European Journal of Operational Research, SIG KDD etc. She has proposed “Instance based credit risk assessment” method in the field of P2P lending, and this method open up a new research perspective in field of risk assessment under the context of big data nowadays. This method got a lot positive ratings and citations by many international famous scholars. International Journal of Operations and Production Management (CABS 4*). He is also an active external examiner for multiple universities in the UK, Ireland and China.
Title: Writing Quality and Soft Information in the GenAI Age: Evidence from Online Credit Markets
Prof. Xiongfeng Pan
Dalian University of Technology, China
Research Area: Regional innovation system, spatial economics and energy economics
Brief introduction: Pan Xiongfeng, with a PhD in Management, is currently a professor and doctoral supervisor at the School of Economics and Management, Dalian University of Technology. He has been selected as one of the "Highly Cited Scholars in China" by Elsevier, ranked among the top 2% of scientists worldwide (released by the Stanford University team), and serves as the deputy editor in chief of the SSCI journal "International Journal of Climate Change Strategies and Management". He is an innovative talent in higher education institutions in Liaoning Province and a scarce talent in local urban development in Dalian. He has led over 50 research projects funded by the National Natural Science Foundation, National Social Science Foundation, Ministry of Education Doctoral Program Fund, and Ministry of Education Humanities and Social Sciences Fund, and has published more than 100 papers in both Chinese and English.
Title: The influencing mechanism of population aging and sub-replacement fertility on carbon efficiency：an empirical analysis based European Economic Area
Abstract: Europe is trapping in serious population aging and sub-replacement fertility condition nowadays, and facing challenges in the aspect of economic growth and carbon emissions. Aging rate and fertility rate prove to be connected with economic growth and carbon emissions, though the relationship is debatable. Related research has rarely focused on carbon efficiency, and ignored the spatial relationship and specific mechanism between aging rate, fertility rate and carbon efficiency. Thus, this paper studies the influence mechanism of aging and fertility rate on carbon efficiency from two perspectives: population flow and technological innovation. The dynamic spatial Durbin model combined with multi-level and multivariate mediation analysis is used to test the data of 29 countries in the European Economic Area from 1998 to 2021. The results show: 1) The effects of both aging rate and fertility rate on carbon efficiency are nonlinear (U-shaped); 2) Technological innovation, immigration and emigration are the mediators of the relationship between aging rate, fertility rate and carbon efficiency, and there are 2 paths; 3) Carbon efficiency, technological innovation, immigration and emigration have snowball and spillover effects that the situation of previous period will significantly affect the performance of the next period, and that of a local region will promote the performance of adjacent countries. The results refresh the EKC theory, and helps policymakers develop feasible policies to achieve the goals in the 2030 Agenda for Sustainable Development and Paris Agreement.